prsim

The wavelet-based quantile mapping (WQM) technique is designed to correct biases in spatio-temporal precipitation forecasts across multiple time scales. The WQM method effectively enhances forecast accuracy by generating an ensemble of precipitation forecasts that account for uncertainties in the prediction process. For a comprehensive overview of the methodologies employed in this package, please refer to Jiang, Z., and Johnson, F. (2023) <doi:10.1029/2022EF003350>. The package relies on two packages for continuous wavelet transforms: 'WaveletComp', which can be installed automatically, and 'wmtsa', which is optional and available from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/wmtsa/>. Users need to manually install 'wmtsa' from this archive if they prefer to use 'wmtsa' based decomposition.

Ze Jiang

Wavelet-Based Quantile Mapping for Postprocessing Numerical
Weather Predictions

Fiona Johnson

prsim function

<dl><dt>modulus</dt>
<dd>Modulus of complex values.</dd>
<dt>phases</dt>
<dd>Argument of complex values.</dd>
<dt>noise_mat</dt>
<dd>Complex matrix from random time series.</dd>
<dt>method</dt>
<dd>Shuffling method, M1: non-shuffling and M2: shuffling. M2 by default.</dd>
<dt>size</dt>
<dd>Block size.</dd>
<dt>seed</dt>
<dd>Seed for shuffling process.</dd></dl>

Arguments

Phase randomization and shuffling — prsim

<dl>

<dt>modulus</dt>
<dd>Modulus of complex values.</dd>


<dt>phases</dt>
<dd>Argument of complex values.</dd>


<dt>noise_mat</dt>
<dd>Complex matrix from random time series.</dd>


<dt>method</dt>
<dd>Shuffling method, M1: non-shuffling and M2: shuffling. M2 by default.</dd>


<dt>size</dt>
<dd>Block size.</dd>


<dt>seed</dt>
<dd>Seed for shuffling process.</dd>

</dl>

prsim: Phase randomization and shuffling

Description

Usage

Value

Arguments